Performance of modified power spectral density features in EEG signal classification

In the present study, a method for classifying the different ictal stages in electroencephalogram (EEG) signals is proposed. The main symptoms of epilepsy are indicated by ictal activities, which trigger widespread neurological disorders other than stroke and thus affect the world population. In this work, a novel ictal classification method that combines the spectral and temporal features of twin components in Hilbert–Huang transform is proposed. Spectral features of instantaneous amplitude (IA) function are obtained based on the power spectral density of autoregressive (AR) modeling. Here four different cases of ictal activities of EEG signal are classified. In each case first and second intrinsic mode function of Hilbert–Huang transform are tabulated. The power spectral density of AR(6) and AR(10) model are done for IA1 and IA2 components of each case. Temporal features of either instantaneous frequency (IF) function or IA are computed. The feature vectors are tested in a well-known database of different classes in interictal, ictal, and normal activities of EEG signals. The discriminating power of each vector is evaluated through one-way analysis of variance, and the classification results are verified using an artificial neural network (ANN) classifier. The performance of the classifier was assessed in term of sensitivity, specificity, and total classification accuracy. The spectral features of the AR(10) of IA and the temporal features of IA yielded 100% accuracy, 100% sensitivity, and 100% specificity in the ictal classification. By contrast, these features obtained only 83.33% of the total classification accuracy in ictal and interictal EEG signal.

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Signal classification by power spectral density: An approach via Riemannian geometry

2012 IEEE Statistical Signal Processing Workshop (SSP) ◽

10.1109/ssp.2012.6319854 ◽

2012 ◽

Cited By ~ 2

Author(s):

Yili Li ◽

K.M. Wong

Keyword(s):

Spectral Density ◽

Power Spectral Density ◽

Riemannian Geometry ◽

Signal Classification ◽

Power Spectral

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Denoising of EEG Signal using Matlab and SIMULINK Techniques and Estimation of Power Spectral Density of EEG Signal using SIMULINK AR Models

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f8956.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 418-422

Keyword(s):

Spectral Density ◽

Power Spectral Density ◽

Signal To Noise Ratio ◽

Standard Technique ◽

Research Paper ◽

Eeg Signal ◽

Eeg Signals ◽

Power Spectral ◽

Electroencephalogram Eeg ◽

Ocular Artifacts

The Electroencephalogram (EEG) is the standard technique for investigating the brain’s electrical activity in different psychological and pathological states. Analysis of Electroencephalogram (EEG) signal is a challenging task by reason of the presence of different artifacts such as Ocular Artifacts (OA) and Electromyogram. Normally EEG signals falls in the frequency range of DC to 60 Hz and amplitude of 1-5 µv. Ocular artifacts do have the similar statistical properties of EEG signals, often interfere with EEG signal, thereby making the analysis of EEG signals more complex. In this research paper, removal of artifacts was done using wavelets (matlab coding) as well as using SIMULINK DWT and IDWT blocks and estimated the SNR. In the next stage the output of IDWT block was taken as input to Burg model and Yule walker model to estimate the power spectral density of EEG signal by setting the various parameters of the blocks. The implementation of denoising of EEG signal using SIMULINK DWT and IDWT blocks and estimation of power spectral density of denoised EEG signal using Burg model and Yule walker model was explained in detail in the paper under the methodology heading. In this research paper, the collected EEG signal is normalized and later linearly mixed with the normalized EOG signal resulting in a noisy EEG signal. This noisy EEG signal is decomposed to 4 levels by using different wavelets. This decomposition of EEG signals yields approximate and detail coefficients. Later different thresholding techniques were applied to detail coefficients and estimated the Signal to Noise Ratio of it and estimated the power spectral density of denoised EEG signal obtained from dB4 wavelet as it is providing better SNR than other wavelets mentioned in the results.

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EEG signal classification of imagined speech based on Riemannian distance of correntropy spectral density

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2020.101899 ◽

2020 ◽

Vol 59 ◽

pp. 101899 ◽

Cited By ~ 3

Author(s):

Mohamad Amin Bakhshali ◽

Morteza Khademi ◽

Abbas Ebrahimi-Moghadam ◽

Sahar Moghimi

Keyword(s):

Spectral Density ◽

Signal Classification ◽

Eeg Signal ◽

Eeg Signal Classification

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Prognosis of Sleep Bruxism Using Power Spectral Density Approach Applied on EEG Signal of Both EMG1-EMG2 and ECG1-ECG2 Channels

IEEE Access ◽

10.1109/access.2019.2924181 ◽

2019 ◽

Vol 7 ◽

pp. 82553-82562 ◽

Cited By ~ 6

Author(s):

Dakun Lai ◽

Md Belal Bin Heyat ◽

Faez Iqbal Khan ◽

Yifei Zhang

Keyword(s):

Spectral Density ◽

Power Spectral Density ◽

Sleep Bruxism ◽

Eeg Signal ◽

Power Spectral

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Olfactory EEG Signal Classification Using a Trapezoid Difference-Based Electrode Sequence Hashing Approach

International Journal of Neural Systems ◽

10.1142/s0129065720500112 ◽

2020 ◽

Vol 30 (03) ◽

pp. 2050011 ◽

Cited By ~ 1

Author(s):

Huirang Hou ◽

Xiaonei Zhang ◽

Qinghao Meng

Keyword(s):

Nearest Neighbor ◽

Classification Performance ◽

Signal Classification ◽

Eeg Signal ◽

Power Spectral ◽

Neuroscience Research ◽

Average Accuracy ◽

Kappa Value ◽

Eeg Signal Classification ◽

Feature Values

Olfactory-induced electroencephalogram (EEG) signal classification is of great significance in a variety of fields, such as disorder treatment, neuroscience research, multimedia applications and brain–computer interface. In this paper, a trapezoid difference-based electrode sequence hashing method is proposed for olfactory EEG signal classification. First, an [Formula: see text]-layer trapezoid feature set whose size ratio of the top, bottom and height is 1:2:1 is constructed for each frequency band of each EEG sample. This construction is based on [Formula: see text] optimized power-spectral-density features extracted from [Formula: see text] real electrodes and [Formula: see text] nonreal electrode’s features. Subsequently, the [Formula: see text] real electrodes’ sequence (ES) codes of each layer of the constructed trapezoid feature set are obtained by arranging the feature values in ascending order. Finally, the nearest neighbor classification is used to find a class whose ES codes are the most similar to those of the testing sample. Thirteen-class olfactory EEG signals collected from 11 subjects are used to compare the classification performance of the proposed method with six traditional classification methods. The comparison shows that the proposed method gives average accuracy of 94.3%, Cohen’s kappa value of 0.94, precision of 95.0%, and F1-measure of 94.6%, which are higher than those of the existing methods.

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